Credibility Classification of Credit Card Clients¶

Group information:¶

Team number: 13
Team members: Chester Wang, HanChen Wang, Qurat-ul-Ain Azim, Renee Kwon

$\href{https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients}{\text{Link to source}}$¶

1. Start by loading the necessary packages and training data set.¶

In [1]:
import pandas as pd
import altair as alt
from pandas_profiling import ProfileReport
from altair_data_server import data_server

# Save a vega-lite spec and a PNG blob for each plot in the notebook
alt.renderers.enable('mimetype')
# Handle large data sets without embedding them in the notebook
alt.data_transformers.enable('data_server')
Out[1]:
DataTransformerRegistry.enable('data_server')
In [2]:
headernames = [
    "ID",
    "LIMIT_BAL",
    "SEX",
    "EDUCATION",
    "MARRIAGE",
    "AGE",
    "PAY_0",
    "PAY_2",
    "PAY_3",
    "PAY_4",
    "PAY_5",
    "PAY_6",
    "BILL_AMT1",
    "BILL_AMT2",
    "BILL_AMT3",
    "BILL_AMT4",
    "BILL_AMT5",
    "BILL_AMT6",
    "PAY_AMT1",
    "PAY_AMT2",
    "PAY_AMT3",
    "PAY_AMT4",
    "PAY_AMT5",
    "PAY_AMT6",
    "default payment next month",
]
train_df = pd.read_csv("../data/split/train.csv", index_col=0, names=headernames, skiprows=1, encoding="utf-8")

2. Basic Exploratory Data Analysis (EDA).¶

Looking at the first and last rows of the training data.

In [3]:
train_df.head()
Out[3]:
ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default payment next month
23637 23638 200000 2 2 2 46 0 0 0 0 ... 136629 139504 142269 4342 4593 14955 5143 5166 5349 0
17169 17170 50000 2 3 2 26 0 0 0 0 ... 26999 27748 28351 1700 2000 3000 1500 1200 1136 0
15955 15956 210000 2 1 2 30 0 0 0 0 ... 5125 5732 9297 2000 2500 2000 1000 4000 4000 0
21486 21487 90000 2 1 2 27 0 0 2 0 ... 50611 52764 55898 4000 0 3000 3000 4000 2043 0
12211 12212 60000 1 2 1 40 0 0 0 0 ... 22564 24191 25778 1500 1500 2000 2000 2000 2000 1

5 rows × 25 columns

In [4]:
train_df.tail()
Out[4]:
ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default payment next month
4426 4427 110000 2 2 1 28 0 0 0 0 ... 45579 46843 48062 2027 2500 2000 2000 2000 1600 0
12695 12696 20000 2 3 2 38 -1 -1 2 0 ... 3668 3508 5278 5015 0 0 3508 5278 780 0
3360 3361 150000 2 2 1 42 0 0 0 0 ... 138016 144084 136604 6022 5022 5100 10500 0 10000 0
18283 18284 190000 1 1 1 54 0 0 0 0 ... 192803 161324 156576 7154 7385 7520 5593 6000 5516 0
28564 28565 100000 2 1 1 36 -2 -2 -2 -2 ... 15156 13920 2099 3514 3400 15156 13920 2099 15638 0

5 rows × 25 columns

There are 24000 records in the training data set and no missing values in any rows or columns.

In [5]:
train_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 24000 entries, 23637 to 28564
Data columns (total 25 columns):
 #   Column                      Non-Null Count  Dtype
---  ------                      --------------  -----
 0   ID                          24000 non-null  int64
 1   LIMIT_BAL                   24000 non-null  int64
 2   SEX                         24000 non-null  int64
 3   EDUCATION                   24000 non-null  int64
 4   MARRIAGE                    24000 non-null  int64
 5   AGE                         24000 non-null  int64
 6   PAY_0                       24000 non-null  int64
 7   PAY_2                       24000 non-null  int64
 8   PAY_3                       24000 non-null  int64
 9   PAY_4                       24000 non-null  int64
 10  PAY_5                       24000 non-null  int64
 11  PAY_6                       24000 non-null  int64
 12  BILL_AMT1                   24000 non-null  int64
 13  BILL_AMT2                   24000 non-null  int64
 14  BILL_AMT3                   24000 non-null  int64
 15  BILL_AMT4                   24000 non-null  int64
 16  BILL_AMT5                   24000 non-null  int64
 17  BILL_AMT6                   24000 non-null  int64
 18  PAY_AMT1                    24000 non-null  int64
 19  PAY_AMT2                    24000 non-null  int64
 20  PAY_AMT3                    24000 non-null  int64
 21  PAY_AMT4                    24000 non-null  int64
 22  PAY_AMT5                    24000 non-null  int64
 23  PAY_AMT6                    24000 non-null  int64
 24  default payment next month  24000 non-null  int64
dtypes: int64(25)
memory usage: 4.8 MB

We have a binary feature, a few categorical features, and numerical features.

In [6]:
train_df.describe()
Out[6]:
ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 ... BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default payment next month
count 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 24000.000000 ... 24000.000000 24000.000000 24000.000000 24000.000000 2.400000e+04 24000.000000 24000.00000 24000.000000 24000.000000 24000.000000
mean 15012.940792 167338.833333 1.602542 1.850875 1.551208 35.516833 -0.012708 -0.130250 -0.163958 -0.220458 ... 43232.172750 40292.983542 38784.349208 5641.261958 5.873889e+03 5116.207083 4742.45800 4722.876500 5188.472375 0.221500
std 8649.751001 129933.404625 0.489382 0.788139 0.521430 9.216367 1.122875 1.196806 1.197450 1.168901 ... 64301.779315 60868.199998 59615.313087 15971.770984 2.209639e+04 15582.324583 15175.92081 15032.940888 17817.373001 0.415265
min 1.000000 10000.000000 1.000000 0.000000 0.000000 21.000000 -2.000000 -2.000000 -2.000000 -2.000000 ... -170000.000000 -81334.000000 -339603.000000 0.000000 0.000000e+00 0.000000 0.00000 0.000000 0.000000 0.000000
25% 7529.750000 50000.000000 1.000000 1.000000 1.000000 28.000000 -1.000000 -1.000000 -1.000000 -1.000000 ... 2395.000000 1761.750000 1261.750000 1000.000000 8.357500e+02 390.750000 285.00000 264.000000 107.750000 0.000000
50% 15014.500000 140000.000000 2.000000 2.000000 2.000000 34.000000 0.000000 0.000000 0.000000 0.000000 ... 18991.000000 18019.000000 16961.000000 2100.000000 2.012500e+03 1818.500000 1500.00000 1500.000000 1500.000000 0.000000
75% 22476.250000 240000.000000 2.000000 2.000000 2.000000 41.000000 0.000000 0.000000 0.000000 0.000000 ... 54519.000000 50218.250000 48990.250000 5006.000000 5.000000e+03 4535.500000 4012.25000 4026.000000 4000.000000 0.000000
max 29999.000000 800000.000000 2.000000 6.000000 3.000000 79.000000 8.000000 7.000000 8.000000 8.000000 ... 706864.000000 823540.000000 699944.000000 505000.000000 1.684259e+06 417588.000000 528897.00000 426529.000000 528666.000000 1.000000

8 rows × 25 columns

3. Group features based on their types and make plots for each type.¶

In [7]:
train_df.columns.tolist()
Out[7]:
['ID',
 'LIMIT_BAL',
 'SEX',
 'EDUCATION',
 'MARRIAGE',
 'AGE',
 'PAY_0',
 'PAY_2',
 'PAY_3',
 'PAY_4',
 'PAY_5',
 'PAY_6',
 'BILL_AMT1',
 'BILL_AMT2',
 'BILL_AMT3',
 'BILL_AMT4',
 'BILL_AMT5',
 'BILL_AMT6',
 'PAY_AMT1',
 'PAY_AMT2',
 'PAY_AMT3',
 'PAY_AMT4',
 'PAY_AMT5',
 'PAY_AMT6',
 'default payment next month']
In [8]:
categorical_features = [
    "EDUCATION",
    "MARRIAGE",
    "PAY_0",
    "PAY_2",
    "PAY_3",
    "PAY_4",
    "PAY_5",
    "PAY_6",
]

binary_features = ["SEX"]

drop = ["ID", "default payment next month"]

numeric_features = [
    "LIMIT_BAL",
    "AGE",
    "BILL_AMT1",
    "BILL_AMT2",
    "BILL_AMT3",
    "BILL_AMT4",
    "BILL_AMT5",
    "BILL_AMT6",
    "PAY_AMT1",
    "PAY_AMT2",
    "PAY_AMT3",
    "PAY_AMT4",
    "PAY_AMT5",
    "PAY_AMT6",
]

Categorical features¶

Education: Ordinal feature. 1 = graduate school; 2 = university; 3 = high school; 4 = others.
Marital status: 1 = married; 2 = single; 3 = others. PAY_X: Ordinal feature. The history of monthly payment tracked from April to September, 2005, as follows: PAY_1 = the repayment status in September, 2005; PAY_2 = the repayment status in August, 2005; . . .;PAY_6 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above.

Binary features¶

Sex is reported as a binary feature (1 = male; 2 = female).

Drop¶

ID duplicate column from the index. default payment next month is the target column.

Numeric features¶

LIMIT_BAL: The amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit.
Age: The age of the individual (years).
BILL_AMTX: Amount of bill statement (NT dollar). BILL_AMT1 = amount of bill statement in September, 2005; BILL_AMT2 = amount of bill statement in August, 2005; . . .; BILL_AMT6 = amount of bill statement in April, 2005.
PAY_AMTX: Amount of previous payment (NT dollar). PAY_AMT1 = amount paid in September, 2005; PAY_AMT2 = amount paid in August, 2005; . . .;PAY_AMT6 = amount paid in April, 2005.

In [9]:
# Plotting categorical features

alt.Chart(train_df).mark_bar(opacity=0.7).encode(
    x=alt.X(alt.repeat()),
    y=alt.Y("count()", title="Count of records"),
    color=alt.Color("default payment next month:N"),
).properties(width=200, height=100).repeat(categorical_features, columns=3)
Out[9]:
In [10]:
# Plotting the binary feature.
alt.Chart(train_df).mark_bar(opacity=0.7).encode(
    x=alt.X("count()", title="Count of records"),
    y=alt.Y("SEX:N", title="SEX"),
    color=alt.Color("default payment next month:N"),
)
Out[10]:
In [11]:
# Plotting numeric features.
alt.Chart(train_df).mark_bar(opacity=0.7).encode(
    x=alt.X(alt.repeat(), bin=alt.Bin(maxbins=30)),
    y=alt.Y("count()", title="Count of records"),
    color=alt.Color("default payment next month:N")
).properties(width=300, height=300).repeat(numeric_features, columns=3)
Out[11]:

4. Looking at the correlation of the features.¶

In [12]:
# Correlation matrix
train_df.corr('spearman').style.format(precision=2).background_gradient()
Out[12]:
  ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2 PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default payment next month
ID 1.00 0.03 0.02 0.03 -0.03 0.03 -0.02 -0.00 -0.01 -0.00 -0.01 -0.00 0.02 0.01 0.02 0.04 0.02 0.02 0.02 0.06 0.09 0.02 0.01 0.04 -0.01
LIMIT_BAL 0.03 1.00 0.06 -0.27 -0.12 0.19 -0.30 -0.34 -0.33 -0.31 -0.28 -0.26 0.06 0.05 0.06 0.08 0.09 0.09 0.28 0.28 0.29 0.29 0.30 0.32 -0.17
SEX 0.02 0.06 1.00 0.02 -0.03 -0.09 -0.06 -0.08 -0.07 -0.07 -0.06 -0.05 -0.05 -0.05 -0.04 -0.03 -0.02 -0.01 -0.01 0.00 0.02 0.01 0.01 0.03 -0.04
EDUCATION 0.03 -0.27 0.02 1.00 -0.16 0.16 0.13 0.17 0.16 0.15 0.14 0.12 0.09 0.09 0.08 0.07 0.06 0.05 -0.05 -0.05 -0.05 -0.05 -0.05 -0.05 0.05
MARRIAGE -0.03 -0.12 -0.03 -0.16 1.00 -0.47 0.02 0.04 0.05 0.05 0.05 0.05 0.01 0.01 0.00 0.01 0.01 0.01 -0.00 -0.02 -0.01 -0.02 -0.01 -0.02 -0.02
AGE 0.03 0.19 -0.09 0.16 -0.47 1.00 -0.07 -0.09 -0.09 -0.08 -0.09 -0.08 0.00 0.00 0.00 -0.00 -0.00 -0.00 0.04 0.05 0.03 0.04 0.04 0.04 0.00
PAY_0 -0.02 -0.30 -0.06 0.13 0.02 -0.07 1.00 0.63 0.55 0.52 0.48 0.46 0.31 0.33 0.31 0.30 0.30 0.29 -0.10 -0.07 -0.06 -0.04 -0.03 -0.05 0.29
PAY_2 -0.00 -0.34 -0.08 0.17 0.04 -0.09 0.63 1.00 0.80 0.71 0.67 0.63 0.57 0.55 0.52 0.49 0.48 0.46 0.02 0.08 0.08 0.09 0.10 0.08 0.21
PAY_3 -0.01 -0.33 -0.07 0.16 0.05 -0.09 0.55 0.80 1.00 0.80 0.72 0.67 0.52 0.59 0.55 0.53 0.51 0.48 0.21 0.03 0.10 0.12 0.12 0.10 0.19
PAY_4 -0.00 -0.31 -0.07 0.15 0.05 -0.08 0.52 0.71 0.80 1.00 0.82 0.73 0.51 0.56 0.62 0.59 0.56 0.53 0.18 0.24 0.07 0.14 0.16 0.14 0.17
PAY_5 -0.01 -0.28 -0.06 0.14 0.05 -0.09 0.48 0.67 0.72 0.82 1.00 0.82 0.50 0.53 0.58 0.65 0.62 0.58 0.17 0.22 0.26 0.11 0.19 0.17 0.16
PAY_6 -0.00 -0.26 -0.05 0.12 0.05 -0.08 0.46 0.63 0.67 0.73 0.82 1.00 0.48 0.52 0.56 0.60 0.67 0.63 0.17 0.20 0.23 0.28 0.14 0.20 0.14
BILL_AMT1 0.02 0.06 -0.05 0.09 0.01 0.00 0.31 0.57 0.52 0.51 0.50 0.48 1.00 0.91 0.86 0.80 0.77 0.73 0.50 0.47 0.44 0.44 0.43 0.41 -0.03
BILL_AMT2 0.01 0.05 -0.05 0.09 0.01 0.00 0.33 0.55 0.59 0.56 0.53 0.52 0.91 1.00 0.91 0.85 0.80 0.76 0.63 0.50 0.47 0.46 0.45 0.43 -0.02
BILL_AMT3 0.02 0.06 -0.04 0.08 0.00 0.00 0.31 0.52 0.55 0.62 0.58 0.56 0.86 0.91 1.00 0.90 0.85 0.81 0.55 0.64 0.49 0.49 0.48 0.46 -0.01
BILL_AMT4 0.04 0.08 -0.03 0.07 0.01 -0.00 0.30 0.49 0.53 0.59 0.65 0.60 0.80 0.85 0.90 1.00 0.90 0.85 0.51 0.55 0.63 0.51 0.50 0.48 -0.01
BILL_AMT5 0.02 0.09 -0.02 0.06 0.01 -0.00 0.30 0.48 0.51 0.56 0.62 0.67 0.77 0.80 0.85 0.90 1.00 0.90 0.48 0.51 0.55 0.65 0.52 0.51 -0.01
BILL_AMT6 0.02 0.09 -0.01 0.05 0.01 -0.00 0.29 0.46 0.48 0.53 0.58 0.63 0.73 0.76 0.81 0.85 0.90 1.00 0.45 0.48 0.52 0.57 0.67 0.53 -0.00
PAY_AMT1 0.02 0.28 -0.01 -0.05 -0.00 0.04 -0.10 0.02 0.21 0.18 0.17 0.17 0.50 0.63 0.55 0.51 0.48 0.45 1.00 0.51 0.52 0.49 0.47 0.46 -0.17
PAY_AMT2 0.06 0.28 0.00 -0.05 -0.02 0.05 -0.07 0.08 0.03 0.24 0.22 0.20 0.47 0.50 0.64 0.55 0.51 0.48 0.51 1.00 0.52 0.52 0.50 0.49 -0.15
PAY_AMT3 0.09 0.29 0.02 -0.05 -0.01 0.03 -0.06 0.08 0.10 0.07 0.26 0.23 0.44 0.47 0.49 0.63 0.55 0.52 0.52 0.52 1.00 0.52 0.54 0.51 -0.15
PAY_AMT4 0.02 0.29 0.01 -0.05 -0.02 0.04 -0.04 0.09 0.12 0.14 0.11 0.28 0.44 0.46 0.49 0.51 0.65 0.57 0.49 0.52 0.52 1.00 0.54 0.55 -0.13
PAY_AMT5 0.01 0.30 0.01 -0.05 -0.01 0.04 -0.03 0.10 0.12 0.16 0.19 0.14 0.43 0.45 0.48 0.50 0.52 0.67 0.47 0.50 0.54 0.54 1.00 0.55 -0.12
PAY_AMT6 0.04 0.32 0.03 -0.05 -0.02 0.04 -0.05 0.08 0.10 0.14 0.17 0.20 0.41 0.43 0.46 0.48 0.51 0.53 0.46 0.49 0.51 0.55 0.55 1.00 -0.12
default payment next month -0.01 -0.17 -0.04 0.05 -0.02 0.00 0.29 0.21 0.19 0.17 0.16 0.14 -0.03 -0.02 -0.01 -0.01 -0.01 -0.00 -0.17 -0.15 -0.15 -0.13 -0.12 -0.12 1.00

5. EDA with Pandas Profiling package.¶

In [13]:
profile = ProfileReport(train_df, title="Pandas Profiling Report")  # , minimal=True)
profile.to_notebook_iframe()

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In [ ]: